Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extragalactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of Sloan Digital Sky Survey galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ∼95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.

诸如Pan-STARRS之类的自主数字天空勘测具有对大量银河和银河外物体成像的能力，并且图像数据的大而复杂性质增强了自动化的使用。在这里，我们描述了星系自动宽形态注释的数据分析过程的设计和实现，并将其应用于Pan-STARRS DR1的数据。该过程基于过滤器，然后是两步卷积神经网络（CNN）分类。训练样本是通过使用一组增强且平衡的手动分类星系来生成的。通过与斯隆数字天空测量星系先前广泛的形态学目录中包含的Pan-STARRS注释进行比较，评估结果的准确性。我们的分析表明，将CNN与多个滤镜组合使用是注释星系和去除不干净图像的有效方法。该目录包含用于1,662,190个星系的形态标记，准确度约为95％。通过选择高于某些置信度阈值的标签，可以进一步提高准确性。该目录是公开可用的。